Ground Precipitation Research Articles

Spatiotemporal evaluation of the GPM satellite precipitation products over the United Arab Emirates

The accurate measurement of rainfall intensity is always challenging due to high spatial and temporal variation of the precipitation, particularly on arid and semiarid regions. The United Arab Emirates (UAE) lies in an arid region with a high shortage of rainfall events. Thus, accurate and continuous monitoring for efficient management of water resources is of particular importance. There are many techniques and instruments that were developed for collecting and/or estimating precipitation. Recently, multi-satellite sensors were globally utilized over the past three decades to measure the precipitation. The Global Precipitation Measurement (GPM) satellite has a capability to detect and measure all types of precipitation using advanced instruments. This article validates the accuracy of the GPM IMERG (Integrated Multi-satellitE Retrievals from Global Precipitation Measurement) early, late and final rainfall products over the UAE using multiple spatial and temporal assessments over the period from Jan 2015 to Dec 2017. The IMERG early-run-product showed a good detection accuracy and low estimation error over the UAE except for the eastern part, while the late-run-product showed a slight improvement. The final-run-product showed the highest detection and low estimation error compared to other products. The correlation between rain-gauge and satellite data demonstrated a contradiction, which reveals that the early-run-product had the highest correlation. Nevertheless, the correlation with the final-run-product was quite lower than the early-run-product. Based on these results, the GPM IMERG final-run-product provides not only a better understanding of the rainfall variability and pattern over the UAE, but also could serve as a reliable product to complementing and/or substituting ground precipitation measurements for ungauged or poorly gauged regions. This paper presents results of one of the earliest assessments of the GPM IMERG products in the Middle East. The outcomes could pave the road for further improvement of the IMERG algorithms in the future.

Monthly assessment of TRMM 3B43 rainfall data with high-density gauge stations over Tunisia

Rainfall data are essential for many environmental applications; however, such data are sparse or not available. The Tropical Rainfall Measuring Mission (TRMM) products are widely used to estimate the rainfall over 75% of the earth and with a spatial resolution of 0.25° (approximately 28 km). The present study was carried out to examine the reliability of remote sensing rainfall data from TRMM data over four governorates (i.e., Gabes, Jendouba, Nabeul, and Kasserine) in Tunisia. Among the TRMM available database, the used data are the monthly products TRMM-3B43 version 7. Two different approaches were conducted to assess the accuracy of satellite rainfall data. The first one is the punctual rainfall control approach, which is based on the calculation of the difference between rainfall data from the TRMM-3B43 product and the ground truth measurements. To do this, we used 149 rainfall stations spread over Tunisia for a period of 16 years (1998–2013). The second approach is based on the spatial autocorrelation analysis of bias between TRMM data and in situ data. Results indicated a good correlation (r≈0.8) between TRMM-3B43data and those derived from weather stations. The highest correlations (r > 0.88) are shown in Jendouba and Nabeul governorates, while Kasserine and Gabes have registered the lowest correlations, with 0.709 and 0.714, respectively. Overall, TRMM remote sensing products tend to overestimate ground precipitation measurements due to the high influence of orographic and climatic parameters on the precipitation accuracy assessment.

Grid box-level evaluation of IMERG over Brazil at various space and time scales

Rainfall data from the Global Precipitation Measurement (GPM) mission provide a new source of information with high spatiotemporal resolution that overcomes the limitations of ground-based rainfall information worldwide. This study evaluates the performance of the Integrated multi-satellitE Retrievals for GPM (IMERG) Final Run product over Brazil by means of multi-temporal and -spatial analyses. The assessment of the IMERG Final Run product is based on six statistics obtained for the period between January–December 2016 (daily, monthly, and annual basis). The analysis consisted of comparing the satellite-based estimates against a ground-based gridded rainfall product created using daily records from 4911 rain gauges distributed throughout Brazil. Overall, the results show that the IMERG product can effectively capture the spatial patterns of rainfall across Brazil. However, the IMERG product presents a slight tendency in overestimating the ground-based rainfall at all timescales. Furthermore, the performance of the satellite product varies throughout the region. The higher errors and biases are found in the North and Central-West regions, but the low density of rain gauges in those regions can be a source of large deviations between IMERG estimates and observations. A large underestimation of the IMERG data is evident along the coastal zone of the North-east region, probably due to the inability of the passive microwave and infrared sensors to detect warm-rain processes over land. This study shows that the IMERG product can be a good source of rainfall data to complement the ground precipitation measurements in most of Brazil, although some uncertainties are found and need to be further studied

A Review on Rainfall Runoff Simulation at Ungauged Catchment

Ungauged catchment occurs when no runoff data are available or when very few ground rain gauges are located in a huge catchment. For these catchments, the parameters to be used in rainfall‐runoff models cannot be attained just by adjusting runoff information and thus should be procured by different techniques. Show parameters that require orientation are normally transposed from comparable measured catchments. The rainfall runoff simulation is very important to estimate and predict the flow in ungauged catchment. This investigation reviews ideas to differentiate hydrological comparability for transposing parameters from a gauged to an ungauged catchment. Model parameters that are physically based are generally derived from other information close to the ungauged catchment of intrigue. The primary challenge with rainfall‐runoff demonstrating in ungauged catchments is the absence of neighborhood ground precipitation and streamflow information to be utilized in aligning the proposed show parameters. Parameter alignment is useful since adjustment can represent the impacts of hydrological set up in a specific catchment. Since hydrological models are especially reliant on their limit conditions, the alignment practice directed can modify the predispositions of info information utilized. Parameters' adjustment can fundamentally improve the execution of rainfall‐runoff models since it included media properties of soil and vegetation which are exceptionally heterogeneous and basically are in every case inadequately known. Alternative methods for ungauged catchments are required which are the subject of this study. This study summarizes the important methods used in an ungauged catchments, discusses the issues of using satellite data as a substitute input to rainfall‐runoff models and its comparison with point scale ground data.

Roles of environmental and spatial factors in structuring assemblages of forest-floor Mesostigmata in the boreal region of Northern Alberta, Canada

ABSTRACTMesostigmatid mites (Arachnida: Parasitiformes) are a diverse, abundant group of soil predators; however, in comparison to some other groups of soil fauna (e.g. oribatid mites), little is known about environmental and spatial processes that influence local species richness and abundance. The main objective of our study was to identify factors strongly correlated with assemblages of Mesostigmata from boreal forests in northern Alberta, Canada. Soil and litter samples came from 62 sites with up to 4 samples per site (216 samples total), with a north–south range of ~800 km between sites. Environmental variables included ground cover type (e.g. moss, lichen, grass), disturbance intensity, precipitation, and temperature. From 3021 individual adult Mesostigmata, we identified 101 species/morphospecies from 21 families, the most species-rich being Ascidae, Zerconidae, and Digamasellidae. Redundancy analysis determined that the environmental variables that correlated most strongly with mite assemblages were precipitation, moss cover, and disturbance intensity. Spatial distance between sites had almost the same explanatory power, as environmental factors. Assemblages became more dissimilar with increasing spatial distance. This study showed the importance that particular environmental and spatial factors likely have on Mesostigmata composition in Canadian boreal forests; however, manipulative studies are required to be able to attribute causation to these correlations.

Evaluation of hydrological utility of IMERG Final run V05 and TMPA 3B42V7 satellite precipitation products in the Yellow River source region, China

Global satellite precipitation products, such as Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with a critical precipitation data source for hydrological applications in data-sparse or ungauged basins. This study conducts a comparative analysis on the quality of the TRMM Multi-satellite Precipitation Analysis 3B42V7 and the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 05 precipitation products and their hydrological utilities in the Yellow River source region (YRSR), a mountainous Alpine region in northwestern China, from April 2014 to December 2016. Results indicate that when evaluated against the ground precipitation observations, IMERG generally improves the daily precipitation estimates relative to its predecessor 3B42V7. In addition, and the correlation coefficients (CCs) of IMERG (0.328 and 0.527) are significantly higher at grid and basin scales than those of 3B42V7 (0.287 and 0.458). However, the 3-hourly precipitation estimates from both products poorly correlate with the ground observations at grid and basin scales. By using the grid-based Xinanjiang (GXAJ) hydrological model calibrated with the gauge-based precipitation for daily streamflow simulations, the 3B42V7-driven model run shows acceptable hydrological simulation skill with regard to the Nash-Sutcliffe model efficiency coefficient (NSE = 0.729), whereas IMERG demonstrates improved performance (NSE = 0.810), which is comparable with the gauge-based simulation (NSE = 0.807). Input-specific model recalibration effectively enhances the hydrological performance of both satellite products (NSE = 0.856 for IMERG and NSE = 0.840 for 3B42V7). Additionally, the gauge-benchmarked GXAJ model with 3B42V7 has limited hydrological skill in simulating three historical flood events at 3-hourly time intervals (NSE = −0.070 to 0.702), while IMERG has a slightly better performance (NSE = 0.266–0.792). Model recalibration also significantly improves the simulations of two out of three flood events, and the NSE skill cores of IMERG (0.581–0.901) and 3B42V7 (−0.795 to 0.852) are relatively lower than that of the gauge-based simulation (0.753–0.969) but comparable. Overall, the IMERG and 3B43V7 satellite precipitation products can be adopted as reliable precipitation sources for hydrological simulations at daily and sub-daily time scales in the study area, with IMERG better suited than 3B42V7. Considering that the low CC values exist in both IMERG and 3B42V7 products, in particular, at a sub-daily temporal scale, the GPM research community should further improve the calibration algorithms and enhance the quality of IMERG products in YRSR. Performing bias-correction of satellite precipitation products is also necessary for hydrological modelers to effectively improve their hydrological utilities.

Spatial and Temporal Variations of Precipitation Extremes and Seasonality over China from 1961~2013

Using the 0.5° × 0.5° gridded Chinese ground precipitation dataset from 1961~2013, spatial and temporal variations in precipitation extremes, total precipitation, the seasonality of precipitation and their linkages in the context of climate change are investigated using the Mann-Kendall trend test, Pettitt change-point test and correlation analysis. The investigation focuses on four extreme indices, i.e., the annual maximum number of consecutive dry days (CDD), the annual maximum number of consecutive wet days (CWD), the annual total precipitation when daily precipitation is greater than 95th percentile (R95pTOT), and the maximum 1-day precipitation (RX1day). The results show that precipitation extremes increased in northwestern China, especially Xinjiang, Tibet and Qinghai (CWD, R95pTOT and RX1day), and scattered parts of southeastern China (R95pTOT and RX1day), but decreased over considerable parts of southwestern China (CWD) and some small parts of northern China (CWD, R95pTOT and RX1day); the spatial patterns of the trends in precipitation extremes and that of total precipitation exhibit considerable similarity over China, which indicates the close relationship between changes in precipitation extremes and total precipitation; change points are detected in different periods ranging from early 1970s to early 2000s for different regions and extreme precipitation indices, and the spatial patterns of the abrupt changes of extreme indices are similar to those of the trends in extreme indices; the concentration index (CI) is strongly positively correlated with R95pTOT and RX1day in most areas in northern China (from the northeast to the northwest) and southwestern China (including Sichuan, Chongqing Guizhou and Guangxi), which means for these regions, the temporal heterogeneity of daily precipitation over a year is dominated by heavy rainfall amounts. The seasonality index of precipitation (SI) is positively related to R95pTOT and RX1day over most areas above 30° N, indicating that heavy precipitation events have a better chance to occur in places with a strong seasonal variation in annual precipitation in these areas, but for most areas below 30° N, the positive relationship is not significant.

Spatial Downscaling of Satellite Precipitation Data in Humid Tropics Using a Site-Specific Seasonal Coefficient

This paper described the development of a spatial downscaling algorithm to produce finer grid resolution for satellite precipitation data (0.05°) in humid tropics. The grid resolution provided by satellite precipitation data (>0.25°) was unsuitable for practical hydrology and meteorology applications in the high hydrometeorological dynamics of Southeast Asia. Many downscaling algorithms have been developed based on significant seasonal relationships, without vegetation and climate conditions, which were inapplicable in humid, equatorial, and tropical regions. Therefore, we exploited the potential of the low variability of rainfall and monsoon characteristics (period, location, and intensity) on a local scale, as a proxy to downscale the satellite precipitation grid and its corresponding rainfall estimates. This study hypothesized that the ratio between the satellite precipitation and ground rainfall in the low-variance spatial rainfall pattern and seasonality region of humid tropics can be used as a coefficient (constant value) to spatially downscale future satellite precipitation datasets. The spatial downscaling process has two major phases: the first is the derivation of the high-resolution coefficient (0.05°), and the second is applying the coefficient to produce the high-resolution precipitation map. The first phase utilized the long-term bias records (1998–2008) between the high-resolution areal precipitation (0.05°) that was derived from dense network of ground precipitation data and re-gridded satellite precipitation data (0.05°) from the Tropical Rainfall Measuring Mission (TRMM) to produce the site-specific coefficient (SSC) for each individual pixel. The outcome of the spatial downscaling process managed to produce a higher resolution of the TRMM data from 0.25° to 0.05° with a lower bias (average: 18%). The trade-off for the process was a small decline in the correlation between TRMM and ground rainfall. Our results indicate that the SSC downscaled method can be used to spatially downscale satellite precipitation data in humid, tropical regions, where the seasonal rainfall is consistent.

A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information

AbstractCompared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, the authors develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, infrared (IR), and water vapor (WV) channels. Specifically, a two-stage framework for precipitation estimation from bispectral information is designed, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the nonzero precipitation amount. In the first stage, the model aims to eliminate the large fraction of NR pixels and to delineate precipitation regions precisely. In the second stage, the model aims to estimate the pointwise precipitation amount accurately while preserving its heavily skewed distribution. Stacked denoising autoencoders (SDAEs), a commonly used deep learning method, are applied in both stages. Performance is evaluated along a number of common performance measures, including both R/NR and real-valued precipitation accuracy, and compared with an operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). For R/NR binary classification, the proposed two-stage model outperforms PERSIANN-CCS by 32.56% in the critical success index (CSI). For real-valued precipitation estimation, the two-stage model is 23.40% lower in average bias, is 44.52% lower in average mean squared error, and has a 27.21% higher correlation coefficient. Hence, the two-stage deep learning framework has the potential to serve as a more accurate and more reliable satellite-based precipitation estimation product. The authors also provide some future directions for development of satellite-based precipitation estimation products in both incorporating auxiliary information and improving retrieval algorithms.

Characterization of Extreme Storm Events Using a Numerical Model–Based Precipitation Maximization Procedure in the Feather, Yuba, and American River Watersheds in California

Abstract Improvements on nonhydrostatic atmospheric models such as MM5 in the last few decades have enhanced our understanding of the precipitation mechanism affected by topography and nonlinear dynamics of the atmosphere. This study addresses the use of such a regional atmospheric model to estimate physical maximum precipitation rates for the next generation of flood management strategies under evolving climate conditions. First, 48 significant historical storm events were selected based on the continuous reconstructed precipitation conditions on the Feather, Yuba, and American River watersheds in California. Then, the boundary conditions of the numerical atmospheric model were modified with the fully saturated atmospheric layers (100% relative humidity) to generate the atmospheric conditions that maximize the precipitation over the three watersheds. Surprisingly, maximizing the atmospheric moisture supply at the model boundary does not always increase the precipitation in the watersheds of interest. A rain-shadow effect of the topography seemed to be intensified by the abundance of the atmospheric moisture in some cases. Consequently, although the linkage between the precipitable water in the atmosphere and the ground precipitation is generally proportional, the alignment of the topography and the wind field can modulate their relationship. Finally, a methodology to maximize the steady-state precipitation rate was discussed to characterize the conceptual continuous heavy storm event in the Feather, Yuba, and American River basins.

Evaluation of Integrated Multisatellite Retrievals for GPM (IMERG) over Southern Canada against Ground Precipitation Observations: A Preliminary Assessment

Abstract The Global Precipitation Measurement (GPM) mission offers new opportunities for modeling a range of physical/hydrological processes at higher resolutions, especially for remote river systems where the hydrometeorological monitoring network is sparse and weather radar is not readily available. In this study, the recently released Integrated Multisatellite Retrievals for GPM [version 03 (V03) IMERG Final Run] product with high spatiotemporal resolution of 0.1° and 30 min is evaluated against ground-based reference measurements (at the 6-hourly, daily, and monthly time scales) over different terrestrial ecozones of southern Canada within a 23-month period from 12 March 2014 to 31 January 2016. While IMERG and ground-based observations show similar regional variations of mean daily precipitation, IMERG tends to overestimate higher monthly precipitation amounts over the Pacific Maritime ecozone. Results from using continuous as well as categorical skill metrics reveal that IMERG shows more satisfactory agreement at the daily and the 6-hourly time scales for the months of June–September, unlike November–March. In terms of precipitation extremes (defined by the 75th percentile threshold for reference data), apart from a tendency toward overdetection of heavy precipitation events, IMERG captured well the distribution of heavy precipitation amounts and observed wet/dry spell length distributions over most ecozones. However, low skill was found over large portions of the Montane Cordillera ecozone and a few stations in the Prairie ecozone. This early study highlights a potential applicability of V03 IMERG Final Run as a reliable source of precipitation estimates in diverse water resources and hydrometeorological applications for different regions in southern Canada.

Evaluation of the gridded CRU TS precipitation dataset with the point raingauge records over the Three-River Headwaters Region

This study evaluated the performance of the gridded CRU TS precipitation dataset in describing the spatial and temporal characteristics of precipitation over the Three-River Headwaters Region (TRHR) during 1961–2014, and the results were compared with those derived from the point raingauge records (RGR) at 29 meteorological stations. It indicated that: (1) temporally, the TRHR has experienced a significant increasing trend in the annual precipitation during 1961–2014; (2) spatially, the mean annual precipitation (MAP) in the TRHR showed the southeast-to-northwest decreasing trend; (3) a close correlation of the MAP with elevation was found, and statistical equations to estimate the MAP derived from the gridded CRU TS dataset were established based on longitude, latitude and elevation. Through comparing the results derived from these two datasets, it is concluded that the CRU TS dataset gave a lower estimation of annual precipitation than that from the RGR data but similar variation characteristics. Moreover, the MAP values derived from the gridded CRU TS dataset could be well estimated by the equations derived from the RGR data. The results would be valuable for the researchers to make better use of this gridded precipitation dataset and have a clearer understanding of the spatio-temporal patterns of precipitation in similar high-elevation mountainous regions such as the TRHR, where ground precipitation measurements are not widely available.

Quantifying water stress effect on daily light use efficiency in Mediterranean ecosystems using satellite data

ABSTRACTThe capacity of six water stress factors (ε′i) to track daily light use efficiency (ε) of water-limited ecosystems was evaluated. These factors are computed with remote sensing operational products and a limited amount of ground data: ε′1 uses ground precipitation and air temperature, and satellite incoming global solar radiation; ε′2 uses ground air temperature, and satellite actual evapotranspiration and incoming global solar radiation; ε′3 uses satellite actual and potential evapotranspiration; ε′4 uses satellite soil moisture; ε′5 uses satellite-derived photochemical reflectance index; and ε′6 uses ground vapor pressure deficit. These factors were implemented in a production efficiency model based on Monteith’s approach in order to assess their performance for modeling gross primary production (GPP). Estimated GPP was compared to reference GPP from eddy covariance (EC) measurements (GPPEC) in three sites placed in the Iberian Peninsula (two open shrublands and one savanna). ε′i were correlated to ε, which was calculated by dividing GPPEC by ground measured photosynthetically active radiation (PAR) and satellite-derived fraction of absorbed PAR. Best results were achieved by ε′1, ε′2, ε′3 and ε′4 explaining around 40% and 50% of ε variance in open shurblands and savanna, respectively. In terms of GPP, R2 ≈ 0.70 were obtained in these cases.

Analysis of isotope element by electrolytic enrichment method for ground water and surface water in Saurashtra region, Gujarat, India

AbstractThe present study has been aimed for the assessment of isotope element Tritium (3H). It is a great threat to human health and environment for lengthy duration. The tritium exists in earth in diverse forms such as (1) small amounts of natural tritium are produced by alpha decay of lithium-7, (2) natural atmospheric tritium is also generated by secondary neutron cosmic ray bombardment of nitrogen, (3) atmospheric nuclear bomb testing in the 1950s, although the contribution from nuclear power plants is small. Tritium or 3H is a radioactive isotope of hydrogen with a half-life of 12.32 ± 0.02 years. Water samples from ground water, surface water, and precipitation were collected from different locations in Gujarat area and were analyzed for the same. Distillation of samples was done to reduce the conductivity. Deuterium and Hydrogen were removed by the process of physico-chemical fractionation in the tritium enrichment unit. The basis of physico-chemical fractionation is the difference in the strength...

Simulation and projection of extreme climate events in China under RCP4.5 scenario

Investigating changes in extreme precipitation is critical for flood management and risk assessment in the context of climate change. Based on China’s Ground Precipitation 0.5° × 0.5° Gridded Dataset (V2.0) and five Global Climate Models (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) provided by ISI-MIP, eight extreme climate event indexes, which are Rx1day, Rx5day, SDII, R10mm, R20mm, CDD, CWD, and R95pTOT, were calculated. This research uses two principles of minimum relative error of mean value and best fit probability distribution to select relative optimal climate models. The results show that the five selected climate models have a better performance in reproducing CDD, R10, RX5D, and SDII, so this research only selected the relative optimal climate models for these four indexes to discuss the future change of extreme precipitation in China; besides, the intensity of precipitation event will be stronger and bigger risk of flood will occur in Huang-huai-hai River Basin in the future. This research is essential for water resources research and the development of strategies of China to adapt to climate change.

A Case Study of Radar Observations and WRF LES Simulations of the Impact of Ground-Based Glaciogenic Seeding on Orographic Clouds and Precipitation. Part II: AgI Dispersion and Seeding Signals Simulated by WRF

AbstractSeveral Weather Research and Forecasting (WRF) Model simulations of natural and seeded clouds have been conducted in non-LES and LES (large-eddy simulation) modes to investigate the seeding impact on wintertime orographic clouds for an actual seeding case on 18 February 2009 in the Medicine Bow Mountains of Wyoming. Part I of this two-part series has shown the capability of WRF LES with 100-m grid spacing to capture the essential environmental conditions by comparing the model results with measurements from a variety of instruments. In this paper, the silver iodide (AgI) dispersion features, the AgI impacts on the turbulent kinetic energy (TKE), the microphysics, and the precipitation are examined in detail using the model data, which leads to five main results. 1) The vertical dispersion of AgI particles is more efficient in cloudy conditions than in clear conditions. 2) The wind shear and the buoyancy are both important TKE production mechanisms in the wintertime PBL over complex terrain in cloudy conditions. The buoyancy-induced eddies are more responsible for the AgI vertical dispersion than the shear-induced eddies are. 3) Seeding has insignificant effects on the cloud dynamics. 4) AgI particles released from the ground-based generators affect the cloud within the boundary layer below 1 km AGL through nucleating extra ice crystals, converting liquid water into ice, depleting more vapor, and generating more precipitation on the ground. The AgI nucleation rate is inversely related to the natural ice nucleation rate. 5) The seeding effects on the ground precipitation are confined within narrow areas. The relative seeding effect ranges between 5% and 20% for the simulations with different grid spacing.

Zonal features of climate regime of the West Siberian Plain and its influence on geosystems

Of particular current importance in the general problem of global climate change is the regional manifestation of this process. The focus of this paper is concentrated on the transitional natural zones: forest tundra (st. Salekhard), and forest-steppe (st. Omsk) of the West Siberian Plain. The study is based on data for ground air temperatures and precipitation at the mean-daily and mean annual level, covering the time interval from 1936 to 2012. The observations are grouped in three periods: from 1936 to 1970, from 1971 to 2006, and from 2001 to 2012 thus making it possible to identify trends in the behavior of mean annual indicators, examine the intra-annual and seasonal changes, and to carry out a typification according to thermal regime and humidification regime. Regional changes in mean annual air temperatures at st. Salekhard and st. Omsk are representative of the general features of temperature variation over the entire observing period. In the Subarctic, however, the warming period became more pronounced in 2001, whereas it started in the 1970s in the forest-steppe, which is borne out by an increase in the recurrence frequency of warm and normal (according to thermal regime) years during the aforementioned periods. The southern part of the West Siberian Plain has experienced a more intense warming than in the north. Analysis of the intra-annual behavior of mean daily temperatures at st. Omsk showed their steady increase during the time interval from 1971 to 2012; this is especially true in regard to a warm season. At st. Salekhard the dynamics of temperatures is more complicated in character. Observation showed comparable (in duration) time spans with alternating increases and decreases in air temperature. The annual precipitation amounts for the time intervals used in the comparison indicated that their mean long-term value in the zones under consideration has increased for the last 35 years by 26 mm at st. Salekhard, and by 15 mm at st. Omsk. Considerable changes of climate have been observed since the 1970s; in high and mid-latitudes, they differ greatly and have influence on phenological phenomena, the structure of seasonal rhythms, the conditions of functioning of landscapes, and on human economic activity.

Historical changes and future projection of extreme precipitation in China

Investigating changes in extreme precipitation, i.e., maximum precipitation for multiday events, is critical for flood management and risk assessment. Based on the observed daily precipitation from China’s Ground Precipitation 0.5° × 0.5° Gridded Dataset (V2.0) and simulated daily precipitation from five general circulation models (GCMs) provided by The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), extreme precipitation indices corresponding to annual maximum 1-, 3-, 15-, and 30-day precipitation across China from 1961 to 2011 and 2011 to 2050 were calculated. Relative changes in the 10-, 20-, and 50-year return period estimates, using 1-, 3-, 15-, and 30-day precipitation, are discussed to represent changes in extreme precipitation in the future. Results show that (1) the spatial distribution of annual maximum precipitation for 1, 3, 15, and 30 days is similar with that of annual precipitation. An increasing trend from the northwest to the southeast was found, with the highest values shown to be in the plain region adjacent to the mountains and coastal area; (2) Comparing the observed and simulated data, it could be seen that climate models have good simulation of 10-, 20-, and 50-year return period events. Absolute relative error is less than 30 % in 80 % in the study area; (3) Extreme precipitation in the future has an increasing trend in China. In the south, extreme precipitation associated with short duration as well as the 50-year return period will likely increase to a comparatively large degree in the future. In the north, extreme precipitation associated with long duration and the 10-year return period will likely see a large increase in the future.

Multiyear monitoring of soil moisture over Iran through satellite and reanalysis soil moisture products

Soil moisture (SM) plays a fundamental role for many hydrological applications including water resources, drought analysis, agriculture, and climate variability and extremes. SM is not measured in most parts of Iran and limited measurements do not meet sufficient temporal and spatial resolution. Hence, due to ease of operation, their global coverage and demonstrated accuracy, use of remote sensing SM products is almost the only way for deriving SM information in Iran. In the present research, surface SM (SSM) datasets at six subregions of Iran with different climate conditions were extracted from two satellite-based passive (SMOSL3) and active+passive (ESA CCI SM) microwave observations, and two reanalysis (ERA-Interim and ERA-Interim/Land) products. Time series of averaged monthly mean SSM products and in situ ground precipitation and temperature measurements were derived for each subregion. Results revealed that, generally, all SSM products were in good agreement with each other with correlation coefficients higher than 0.5. The better agreement was found in the Northeast and Southwest region with average correlation values equal to 0.88 and 0.91, respectively. It should be noted that the SSM datasets are characterized by different periods and lengths. Hence, results should be assessed with cautious. Moreover, most SSM products have strong correlations with maximum, minimum and average temperature as well as with total monthly precipitation. Also, trend analysis showed no trend for time series of monthly SSM over all subregions in the two periods 1980–1999 and 2000–2014. The only exceptions were the Southeast subregion for ERA-Interim and Center and Northwest subregions for the ESA CCI SM for which a negative trend was detected for the period 2000–2014. Finally, the Standardized Soil Moisture Index (SSI) calculated from ERA-Interim, ERA-I/Land and ESA CCI SM datasets showed that the Center and Southeast regions suffered from the most severe and longest-lasting drought events in the last decade.

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground conditions and precipitation regimes (from natural to agricultural surfaces and from desert to humid regions) and provide long-term in-situ data. One of the algorithms is the artificial neural network-based algorithm developed by the Institute of Applied Physics of the National Research Council (IFAC-CNR) (HydroAlgo) and the second one is the Single Channel Algorithm (SCA) developed by USDA-ARS (US Department of Agriculture-Agricultural Research Service). Both algorithms are based on the same radiative transfer equations but are implemented very differently. Both made use of datasets provided by the Japanese Aerospace Exploration Agency (JAXA), within the framework of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Global Change Observation Mission–Water GCOM/AMSR-2 programs. Results demonstrated that both algorithms perform better than the mission specified accuracy, with Root Mean Square Error (RMSE) ≤0.06 m3/m3 and Bias <0.02 m3/m3. These results expand on previous investigations using different algorithms and sites. The novelty of the paper consists of the fact that it is the first intercomparison of the HydroAlgo algorithm with a more traditional retrieval algorithm, which offers an approach to higher spatial resolution products.